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Found 9 papers, 6 papers with code
Improving Deep Neural Network Classification Confidence using Heatmap-based eXplainable AI
This paper quantifies the quality of heatmap-based eXplainable AI (XAI) methods w. r. t image classification problem.
Self Reward Design with Fine-grained Interpretability
This paper proposes a way to circumvent the issues through the bottom-up design of neural networks with detailed interpretability, where each neuron or layer has its own meaning and utility that corresponds to humanly understandable concept.
Two Instances of Interpretable Neural Network for Universal Approximations
This paper proposes two bottom-up interpretable neural network (NN) constructions for universal approximation, namely Triangularly-constructed NN (TNN) and Semi-Quantized Activation NN (SQANN).
A Modified Convolutional Network for Auto-encoding based on Pattern Theory Growth Function
This brief paper reports the shortcoming of a variant of convolutional neural network whose components are developed based on the pattern theory framework.
Convolutional Neural Network Interpretability with General Pattern Theory
Ongoing efforts to understand deep neural networks (DNN) have provided many insights, but DNNs remain incompletely understood.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic Dataset
Heatmaps can be appealing due to the intuitive and visual ways to understand them but assessing their qualities might not be straightforward.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Generalization on the Enhancement of Layerwise Relevance Interpretability of Deep Neural Network
The practical application of deep neural networks are still limited by their lack of transparency.
Enhancing the Extraction of Interpretable Information for Ischemic Stroke Imaging from Deep Neural Networks
We implement a visual interpretability method Layer-wise Relevance Propagation (LRP) on top of 3D U-Net trained to perform lesion segmentation on the small dataset of multi-modal images provided by ISLES 2017 competition.
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI
Unfortunately, the blackbox nature of the deep learning is still unresolved, and many machine decisions are still poorly understood.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
